SoK: Unifying Cybersecurity and Cybersafety of Multimodal Foundation Models with an Information Theory Approach

• URL: http://arxiv.org/abs/2411.11195v2
• Date: Tue, 19 Nov 2024 11:22:23 GMT
• Title: SoK: Unifying Cybersecurity and Cybersafety of Multimodal Foundation Models with an Information Theory Approach
• Authors: Ruoxi Sun, Jiamin Chang, Hammond Pearce, Chaowei Xiao, Bo Li, Qi Wu, Surya Nepal, Minhui Xue,
• Abstract summary: Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence. This paper conceptualizes cybersafety and cybersecurity in the context of multimodal learning. We present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats.
• Score: 58.93030774141753
• License:
• Abstract: Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence, combining diverse data modalities to enhance learning and understanding across a wide range of applications. However, this integration also brings unique safety and security challenges. In this paper, we conceptualize cybersafety and cybersecurity in the context of multimodal learning and present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats to these models. We propose a taxonomy framework grounded in information theory, evaluating and categorizing threats through the concepts of channel capacity, signal, noise, and bandwidth. This approach provides a novel framework that unifies model safety and system security in MFMs, offering a more comprehensive and actionable understanding of the risks involved. We used this to explore existing defense mechanisms, and identified gaps in current research - particularly, a lack of protection for alignment between modalities and a need for more systematic defense methods. Our work contributes to a deeper understanding of the security and safety landscape in MFMs, providing researchers and practitioners with valuable insights for improving the robustness and reliability of these models.

Related papers

• New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook [54.24701201956833]
  Security and privacy issues have undermined users' confidence in pre-trained models. Current literature lacks a clear taxonomy of emerging attacks and defenses for pre-trained models. This taxonomy categorizes attacks and defenses into No-Change, Input-Change, and Model-Change approaches.
  arXiv  Detail & Related papers  (2024-11-12T10:15:33Z)
• A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation [0.3413711585591077]
  As generative AI systems, including large language models (LLMs) and diffusion models, advance rapidly, their growing adoption has led to new and complex security risks. This paper introduces a novel formal framework for categorizing and mitigating these emergent security risks. We identify previously under-explored risks, including latent space exploitation, multi-modal cross-attack vectors, and feedback-loop-induced model degradation.
  arXiv  Detail & Related papers  (2024-10-15T02:51:32Z)
• Diffusion Models for Offline Multi-agent Reinforcement Learning with Safety Constraints [0.0]
  We introduce an innovative framework integrating diffusion models within the Multi-agent Reinforcement Learning paradigm. This approach notably enhances the safety of actions taken by multiple agents through risk mitigation while modeling coordinated action.
  arXiv  Detail & Related papers  (2024-06-30T16:05:31Z)
• Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks [2.28438857884398]
  Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges. This study proposes an innovative security framework inspired by Control-Flow (CFA) mechanisms, traditionally used in cybersecurity. We authenticate and verify the integrity of model updates across the network, effectively mitigating risks associated with model poisoning and adversarial interference.
  arXiv  Detail & Related papers  (2024-03-15T04:03:34Z)
• The Art of Defending: A Systematic Evaluation and Analysis of LLM Defense Strategies on Safety and Over-Defensiveness [56.174255970895466]
  Large Language Models (LLMs) play an increasingly pivotal role in natural language processing applications. This paper presents Safety and Over-Defensiveness Evaluation (SODE) benchmark.
  arXiv  Detail & Related papers  (2023-12-30T17:37:06Z)
• Security and Privacy Issues of Federated Learning [0.0]
  Federated Learning (FL) has emerged as a promising approach to address data privacy and confidentiality concerns. This paper presents a comprehensive taxonomy of security and privacy challenges in Federated Learning (FL) across various machine learning models.
  arXiv  Detail & Related papers  (2023-07-22T22:51:07Z)
• Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements [76.80453043969209]
  This survey presents a framework for safety research pertaining to large models. We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models. We explore the strategies for enhancing large model safety from training to deployment.
  arXiv  Detail & Related papers  (2023-02-18T09:32:55Z)
• Holistic Adversarial Robustness of Deep Learning Models [91.34155889052786]
  Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models.
  arXiv  Detail & Related papers  (2022-02-15T05:30:27Z)
• Towards a Robust and Trustworthy Machine Learning System Development [0.09236074230806578]
  We present our recent survey on the state-of-the-art ML trustworthiness and technologies from a security engineering perspective. We then push our studies forward above and beyond a survey by describing a metamodel we created that represents the body of knowledge in a standard and visualized way for ML practitioners. We propose future research directions motivated by our findings to advance the development of robust and trustworthy ML systems.
  arXiv  Detail & Related papers  (2021-01-08T14:43:58Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.